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Note The Incremental Save Percentage option may prevent a purge during a drawing session from removing all unreferenced layers or linetypes from the drawing. To avoid this, you can either set incremental save to zero, or save the drawing and reopen it and then immediately use the PURGE command. To set this option, choose Tools Options Open and Save tab.

- the current measurement error is at the most 2 5% = 10% , which is lower than the setting threshold at 20%. Spurious tripping is therefore unlikely to occur.

Customizing the Interface . . The preference panels . General preferences . . Saving Files . . . . . . . Display & Cursors . . . Transparency & Gamut Units & Rulers . . . . . Guides & Grid . . . . . . Plug-Ins & Scratch Disk Image Cache. . . . . . . Physical memory usage

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CActive provides a virtual RunError() method which the active scheduler calls if a leave occurs in the RunL() method of the active object. The method takes the leave code as a parameter and returns an error code to indicate whether the leave has been handled. The default implementation does not handle the leave and simply returns the leave code passed to it. If the active object can handle any leaves occurring in RunL(), it should do so by overriding the default implementation and returning KErrNone to indicate that the leave has been handled.

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Wireless communication has really grown in the past few years. Many businesses, universities, and even some cities have now implemented wireless access for anyone to use. There is nothing like being able to sit in a bookstore or a coffee shop and being able to connect to the Internet and all that it offers. Signals in wireless communication are sent via antennas, microwave stations, satellite, or infrared light. Wireless communication enables data to be transferred through the air via a communication signal. Communication is normally handled by infrared light or high-frequency radio waves. Infrared communication normally takes place between nodes. The wireless signal between a PDA and a PC is an example of nodes that use an infrared signal. Data communications, radio, and cellular phones are all examples of nodes that use radio waves for data communication. Section 3.3.3.9.3 covers the hardware that makes wireless communication as a transmission medium a reality.

All the earlier phases in the SHAMPU (Shape, Harness, and Manage Project Uncertainty) process necessarily involve some structuring of identi ed issues The structure phase is concerned with reviewing and extending this earlier structuring The objective is to improve understanding of the relative importance of different sources given identi ed responses, to explore the interactions between issues, and to test the assumptions implicit or explicit in all earlier steps This can lead to re nement of existing responses and prompt the development of new, more effective responses It can also lead to new models or analyses In general, we want the structure used to be as simple as possible, but not misleadingly so The structure phase involves testing the simplifying assumptions and developing a more complex structure when necessary Failure to do so can render project risk management dangerously misleading.

So far we have discussed two-dimensional image compression. However, acquisition of three- and four-dimensional (3-D, 4-D) medical images is becoming increasingly common in CT, MR, US, and DSA (s 4 and 5). The third dimension considered in this section can be in the spatial domain (e.g., sectional images) or in the time domain (e.g., in an angiographic study with time as the third dimension). The acquisition processes signi cantly raises the volume of data gathered per study. To compress 3-D data ef ciently, one must consider decorrelation images. Some earlier works done on 3-D compression reported by Sun and Goldberg (1988), Lee (1993), and Koo (1992) included correlation between adjacent sections. Chan, Lou, and Huang (1989) reported a full-frame discrete Fourier transform (DCT) method for DSA, CT, and MRI. They found that by grouping four to eight slices as a 3-D volume, compression was twice as ef cient as it was with 2-D full-frame DCT for DSA. The 3-D method of compressing CT images was also more ef cient than 2-D. However, 3-D compression did not achieve very high ef ciency in the case of MR images. The publication of works by Daubechies (1988) and Mallat (1989) on the use of wavelet transform for image compression attracted a lot of attention. The primary advantage of the wavelet transform compared with the cosine transform is that wavelet transform is localized both in the 3-D spatial domain and the 2-D spatial

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